Enhanced agricultural carbon sinks provide benefits for farmers and the climate

Carbon sequestration on agricultural land, albeit long-time neglected, offers substantial mitigation potential. Here we project, using an economic land-use model, that these options offer cumulative mitigation potentials comparable to afforestation by 2050 at 160 USD2022 tCO2 equivalent (tCO2e−1), with most of it located in the Global South. Carbon sequestration on agricultural land could provide producers around the world with additional revenues of up to 375 billion USD2022 at 160 USD2022 tCO2e−1 and allow achievement of net-zero emissions in the agriculture, forestry and other land-use sectors by 2050 already at economic costs of around 80–120 USD2022 tCO2e−1. This would, in turn, decrease economy-wide mitigation costs and increase gross domestic product (+0.6%) by the mid-century in 1.5 °C no-overshoot climate stabilization scenarios compared with mitigation scenarios that do not consider these options. Unlocking these potentials requires the deployment of highly efficient institutions and monitoring systems over the next 5 years across the whole world, including sub-Saharan Africa, where the largest mitigation potential exists.


GLOBIOM-G4M
The Global Biosphere Management Model (GLOBIOM) (IBF-IIASA 2023) is a partial equilibrium model that covers the global agricultural and forestry sectors, including the bioenergy sector.
Commodity markets and international trade are represented at the level of 37 economic regions in this study.Prices are endogenously determined at the regional level to establish market equilibrium to reconcile demand, domestic supply and international trade.The spatial resolution of the supply side relies on the concept of Simulation Units, which are aggregates of 5 to 30 arcmin pixels belonging to the same altitude, slope, and soil class, and also the same country (Skalský et al. 2008).For crops, livestock, and forest products, spatially explicit Leontief production functions covering alternative production systems are parameterized using biophysical models like EPIC (Environmental Policy Integrated Model) (Williams 1995), G4M (Global Forest Model) (Kindermann, McCallum, et al. 2008;Gusti 2010), or the RUMINANT model (Herrero et al. 2013).For the present study, the supply side spatial resolution was aggregated to 2 degrees (about 200 x 200 km at the equator).Land and other resources are allocated to the different production and processing activities to maximize a social welfare function which consists of the sum of producer and consumer surplus.The model includes six land cover types: cropland, grassland, short rotation plantations, managed forests, unmanaged forests, and other natural vegetation land.Depending on the relative profitability of primary, by-, and final products production activities, the model can switch from one land cover type to another.Spatially explicit land conversion at the simulation unit level over the simulation period is endogenously determined within the available land resources and conversion costs.Land conversion cost parameters are region specific to match observed land use changes.Land conversion possibilities are further restricted through biophysical land suitability and production potentials, and through a matrix of potential land cover transitions.Land conversion from one land cover to the other takes place if the marginal revenue of the new production activity exceeds the marginal land conversion cost.GLOBIOM covers major GHG emissions from agricultural production, forestry, and other land use including CO2 emissions from above-and belowground biomass changes, N 2 O from the application of synthetic fertilizer and manure to soils, N 2 O from manure dropped on pastures, CH 4 from rice cultivation, N 2 O and CH 4 from manure management, and CH 4 from enteric fermentation.CO 2 emissions/removals from afforestation, deforestation, and wood production in managed forests are estimated by geographically explicit (0.5x0.5 degree) model G4M (Kindermann, Obersteiner, et al. 2008;Gusti 2010) that is connected with GLOBIOM.Afforestation and deforestation decisions are calculated by comparing net present values of agriculture and forestry land uses.Afforestation occurs where it is more profitable than the agriculture and the environmental conditions are suitable for forest growth.Deforestation, in contrast, happens where agriculture net present value plus profit from one-time selling of deforested wood exceeds the net present value of forestry.The net present values are estimated considering agriculture land rents and wood prices obtained from GLOBIOM and price of carbon stored in biomass.The land transitions in G4M are harmonized with GLOBIOM agriculture land demand.G4M simulates forest management aimed at sustainable production of wood demanded by GLOBIOM at regional scale.
In Table S1 we refer to relevant references for detailed information on the main model structure, datasets used, or individual modules.
Table S1.Key references for model documentation.1.2 Scenario development

Baseline
The baseline scenario corresponds to the SSP2 middle-of-the-road scenario without land-based climate mitigation efforts (Fricko et al. 2017).Population and GDP projections were implemented in GLOBIOM based on the SSP database (https://tntcat.iiasa.ac.at/SspDb/).Income elasticities are calibrated to mimic FAO projections of diets (Alexandratos and Bruinsma 2012).We assume moderate reductions in food waste and losses over time add to the availability of agricultural products (FAO 2011).Technological change for crops is based on crop specific yield response functions to GDP per capita growth estimated for different income groups using a fixed effects model (Havlík et al. 2014).
Fertilizer use and costs of agricultural production increase in proportion with yields.Improvements in livestock feed conversion efficiencies follow Bouwman et al. (2005).Transition towards more efficient livestock production systems takes place at a moderately fast pace.Biomass demand for bioenergy is projected to remain rather stable at around 60 EJ/yr until mid-century based on MESSAGE-GLOBIOM projections.Details on the SSP drivers and scenario implementation are provided in (Fricko et al. 2017).using the US GDP deflator from the World Bank.This simplified approach does not capture differences in regional macro-economic developments.However, the proportional scaling ensures consistency of the presented results with the underlying partial equilibrium modelling framework which was performed in constant USD 2000 .Non-CO 2 gases were converted to CO 2 equivalents (CO 2 e) using global warming potentials from the 4 th IPCC Assessment Report (298 for N 2 O, 25 for CH 4 ).The following gases were included under the GHG price:

AFOLU sector GHG mitigation scenarios
• Agricultural N 2 O emissions: synthetic fertilizer, manure applied to soils and dropped on pastures, manure management • Agricultural CH 4 emissions: rice cultivation, enteric fermentation, manure management • Agricultural CO 2 removals: soil carbon sequestration from improved cropland and grassland management, above-and belowground biomass carbon sequestration from silvo-pasture systems, emission reduction (mainly CO 2 ) from biochar application on cropland • Forestry CO 2 emissions/removals: afforestation, deforestation, forest management, and other land use changes Emissions from organic soils, agricultural residue burning, and savannah burning were assumed to be kept constant over time.

Economy-wide climate stabilization scenarios
To assess the impact of agricultural CO 2 sequestration options on the economy-wide mitigation portfolio, we quantified two 1.5 °C mitigation scenarios in MESSAGEix-GLOBIOM (Fricko et al. 2017;Krey et al. 2020) with-and without considering agricultural CO 2 sequestration options in the AFOLU mitigation potentials.MESSAGEix (Model for Energy Supply Strategy Alternatives and their General Environmental Impact), is a linear programming system engineering model used for mediumto long-term energy system planning, energy policy analysis, and scenario development (Huppmann et al. 2019;Messner and Strubegger 1995).The model provides a framework for representing an energy system with all its interdependencies from resource extraction, imports and exports, conversion, transport, and distribution, to the provision of energy end-use services such as light, space conditioning, industrial production processes, and transportation.MESSAGEix is linked to GLOBIOM to assess the implications of utilizing bioenergy of different types and to integrate the GHG emissions from energy and land use (Fricko et al. 2017) and to the aggregated single-sector macro-economic model MACRO to assess economic implications and economy-wide feedbacks (Messner and Schrattenholzer 2000).
MACRO is a macroeconomic model maximizing the intertemporal utility function of a single representative producer-consumer in each world region and is used to generate a consistent economic response (e.g., changes in GDP or household consumption) to changes in energy prices (from MESSAGEix) because of energy or climate policies.
For this study, we quantified two 1.5 °C scenario based on the EN_NPi2020_600 no-overshoot scenario with a remaining carbon budget of 600 GtCO 2 from the ENGAGE project (Riahi et al. 2021;Hasegawa et al. 2021) in MESSAGEix-GLOBIOM that considered or did not consider CO 2 sequestration options on agricultural land in the AFOLU mitigation potentials.

Additional parameters
stabilization pathways using MESSAGEix-GLOBIOM, land-based mitigation requirements (carbon prices, bioenergy demands) Rogelj et al. (2018),Hasegawa et al. (2021) On top of the baseline, we implemented eight climate change mitigation scenarios differentiated by a GHG price trajectory on agriculture, forestry, and other land use (AFOLU) emissions/removals.The GHG prices were implemented as off 2020 as an additional cost for emissions (or subsidy for removals) reaching a GHG price of25, 50, 75, 100, 125, 150, 175, and 200 USD 2000  /tCO 2 e by 2050.GHG prices were converted ex-post from USD 2000 to USD 2022 applying a global uniform conversion rate of 1.63

Table S2 .
Carbon price and bioenergy demand trajectories over time.